Quest Commercial Scale CCS – The First Year
Bibliographic record
Abstract
Abstract Quest is a commercial scale, fully integrated carbon capture and storage project and the first connected to the oil sands. Located in Alberta, Canada, Quest is designed to capture and safely store more than one million tonnes of CO2 each year – equal to the emissions from about 250,000 cars. This represents one-third of the emissions from the Scotford Upgrader, which transforms bitumen into crude for refining into fuel and other products. CO2 injection began on the 23rd of August 2015 and the project has successfully captured and stored over 1 million tonnes of CO2 in its first year of operation. Startup was successful and followed a staged approach. First, the three capture units were commissioned with a period of run-in, then the compression and conditioning, followed by displacement of the nitrogen from the pipeline, before finally moving to injecting around 3000 tonnes per day into the Basal Cambrian Sandstone saline aquifer; using two of three available wells. The injectivity and pressure dissipation has been exceptionally good allowing the third well to be reserved for interference testing. During the start of injection, the production technology team worked in the Scotford control room and ran real time transient flow simulations of the CO2 expansion across the well head chokes and into the wells. This allowed the operators to pro-actively manage the ramp up. To our knowledge this is the first time that this has been done. The onshore storage is deploying cutting edge monitoring technology – fibre optic vertical seismic profiles and line of sight surface CO2 detection; along with microsesimic and extensive pressure monitoring. This paper outlines the experience with starting up the facilities and wells and will present the results of the first year of operation. It will present the experience from stakeholder engagement, the capture plant, the compression system, pipeline, wells and monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".